This package provides tools for extracting and analyzing cyclic signals from time series.

This package provides functions aiming to facilitate the analysis of the structure of animal acoustic signals in R'. warbleR makes use of the basic sound analysis tools from the packages tuneR and seewave', and offers new tools for exploring and quantifying acoustic signal structure. The package allows to organize and manipulate multiple sound files, create spectrograms of complete recordings or individual signals in different formats, run several measures of acoustic structure, and characterize different structural levels in acoustic signals (Araya-Salas et al 2016 <doi:10.1111/2041-210X.12624>).

This package provides inference for the Wilcoxon-Mann-Whitney test under the null hypothesis H0: AUC = 0.5 for continuous, discrete or mixed random variables. Traditional implementations test H0: F = G, which is inappropriately broad and leads to erroneous inferences. Methods are described in M. Grendar (2025) "Wilcoxon-Mann-Whitney Test of No Group Discrimination" <doi:10.48550/arXiv.2511.20308>.

This package provides a comprehensive suite of functions for processing, analyzing, and visualizing textual data from tweets is offered. Users can clean tweets, analyze their sentiments, visualize data, and examine the correlation between sentiments and environmental data such as weather conditions. Main features include text processing, sentiment analysis, data visualization, correlation analysis, and synthetic data generation. Text processing involves cleaning and preparing tweets by removing textual noise and irrelevant words. Sentiment analysis extracts and accurately analyzes sentiments from tweet texts using advanced algorithms. Data visualization creates various charts like word clouds and sentiment polarity graphs for visual representation of data. Correlation analysis examines and calculates the correlation between tweet sentiments and environmental variables such as weather conditions. Additionally, random tweets can be generated for testing and evaluating the performance of analyses, empowering users to effectively analyze and interpret Twitter data for research and commercial purposes.

Implementation of integrative weighting approaches for multiple observational studies and causal inferences. The package features three weighting approaches, each representing a special case of the unified weighting framework, introduced by Guha and Li (2024) <doi:10.1093/biomtc/ujae070>, which includes an extension of inverse probability weights for data integration settings.

Calculate magnetic field at a given location and time according to the World Magnetic Model (WMM). Both the main field and secular variation components are returned. This functionality is useful for physicists and geophysicists who need orthogonal components from WMM. Currently, this package supports annualized time inputs between 2000 and 2025. If desired, users can specify which WMM version to use, e.g., the original WMM2015 release or the recent out-of-cycle WMM2015 release. Methods used to implement WMM, including the Gauss coefficients for each release, are described in the following publications: Chulliat et al (2020) <doi:10.25923/ytk1-yx35>, Chulliat et al (2019) <doi:10.25921/xhr3-0t19>, Chulliat et al (2015) <doi:10.7289/V5TB14V7>, Maus et al (2010) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/WMM2010_Report.pdf>, McLean et al (2004) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/TRWMM_2005.pdf>, and Macmillian et al (2000) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/wmm2000.pdf>.

This package provides a multivariate weather generator for daily climate variables based on weather-states (Flecher et al. (2010) <doi:10.1029/2009WR008098>). It uses a Markov chain for modeling the succession of weather states. Conditionally to the weather states, the multivariate variables are modeled using the family of Complete Skew-Normal distributions. Parameters are estimated on measured series. Must include the variable Rain and can accept as many other variables as desired.

The Wordle game. Players have six attempts to guess a five-letter word. After each guess, the player is informed which letters in their guess are either: anywhere in the word; in the right position in the word. This can be used to inform the next guess. Can be played interactively in the console, or programmatically. Based on Josh Wardle's game <https://www.powerlanguage.co.uk/wordle/>.

This package provides tools for constructing, simulating, and analyzing large-scale water resources systems. The package provides functions to represent system components such as reservoirs, aquifers, rivers, diversions, and demand sites, and to simulate system behavior under Standard Operating Policy. It also supports the development and evaluation of water allocation strategies and hydropower operations within integrated water resources systems.

Weighted Piecewise Kernel Density Estimation for large data.

The main functionalities of wrappedtools are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.

This package implements a permutation test method for the weighted quantile sum (WQS) regression, building off the gWQS package (Renzetti et al. <https://CRAN.R-project.org/package=gWQS>). Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. 2015 <doi:10.1007/s13253-014-0180-3>). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. 2022 <doi:10.1289/EHP10570>).

Download and search data from the World Bank APIs, including the Indicators API, the Poverty and Inequality Platform (PIP) API, the Finances One API, and the Projects API. See <https://datahelpdesk.worldbank.org/knowledgebase/articles/889386-developer-information-overview> for further details.

Queries online WikiPathway graphics and allows mapping user data (e.g., expression values) on the graph. The package designs a grammar of graphic syntax that using pipe operator to add graphic layer.

The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test. This package can be used with any test dataset on which you have computed prior probabilities of case status, posterior probabilities of case status, and you have the observed case-control status. In comparison with the C-statistic (area under ROC curve), the expected weight of evidence (expected information for discrimination) has several advantages as a summary measure of predictive performance. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.

Easily collect walk scores, bike scores, and transit scores (where available) from the Walk Score API <https://www.walkscore.com/professional/api.php>, a proprietary API that assigns locations a walkability score between 0 and 100.

Shinohara (2014) <doi:10.1016/j.nicl.2014.08.008> introduced WhiteStripe', an intensity-based normalization of T1 and T2 images, where normal appearing white matter performs well, but requires segmentation. This method performs white matter mean and standard deviation estimates on data that has been rigidly-registered to the MNI template and uses histogram-based methods.

Estimates the standard and weighted Elo (WElo, Angelini et al., 2022 <doi:10.1016/j.ejor.2021.04.011>) rates. The current version provides Elo and WElo rates for tennis, according to different systems of weights (games or sets) and scale factors (constant, proportional to the number of matches, with more weight on Grand Slam matches or matches played on a specific surface). Moreover, the package gives the possibility of estimating the (bootstrap) standard errors for the rates. Finally, the package includes betting functions that automatically select the matches on which place a bet.

Monetary valuation of wood in German forests (stumpage values), including estimations of harvest quantities, wood revenues, and harvest costs. The functions are sensitive to tree species, mean diameter of the harvested trees, stand quality, and logging method. The functions include estimations for the consequences of disturbances on revenues and costs. The underlying assortment tables are taken from Offer and Staupendahl (2018) with corresponding functions for salable and skidded volume derived in Fuchs et al. (2023). Wood revenue and harvest cost functions were taken from v. Bodelschwingh (2018). The consequences of disturbances refer to Dieter (2001), Moellmann and Moehring (2017), and Fuchs et al. (2022a, 2022b). For the full references see documentation of the functions, package README, and Fuchs et al. (2023). Apart from Dieter (2001) and Moellmann and Moehring (2017), all functions and factors are based on data from HessenForst, the forest administration of the Federal State of Hesse in Germany.

Establishes and maintains vertex pool topology for geometry handled by wk'. Segments are the atomic unit, vertices are shared via integer references into a pool. Topology is made discoverable via coincident vertex detection while not requiring modification of the input data. Topological data models follow principles described in Worboys and Duckham (2004, ISBN:978-0415283755). The edge-based topology geometry decomposed into vertices and directed edge pairs is a simplification of the quad-edge case in Guibas & Stolfi (1985) <doi:10.1145/282918.282923>.

This package provides functions that allow for accessing domains and a number of search engines.

Process GPS and accelerometry data to generate walk bouts. A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the walkboutr package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) <doi:10.1016/j.trd.2017.09.026>.

This package implements a spatiotemporal boundary detection model with a dissimilarity metric for areal data with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and spatial correlation is introduced at each time point through a conditional autoregressive (CAR) prior. Temporal correlation is introduced through a hierarchical structure and can be specified as exponential or first-order autoregressive. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method", by Berchuck et al (2019) <doi:10.1080/01621459.2018.1537911>.

This package provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) <doi:10.1016/j.amc.2017.05.046>), scale index and windowed scale index (Benitez et al. (2010) <doi:10.1016/j.camwa.2010.05.010>).